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Sensors 2017, 17(7), 1692; https://doi.org/10.3390/s17071692

Angular Rate Sensing with GyroWheel Using Genetic Algorithm Optimized Neural Networks

Control and Simulation Center, Harbin Institute of Technology, Harbin 150080, China
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Received: 13 June 2017 / Revised: 19 July 2017 / Accepted: 20 July 2017 / Published: 22 July 2017
(This article belongs to the Special Issue Inertial Sensors for Positioning and Navigation)
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Abstract

GyroWheel is an integrated device that can provide three-axis control torques and two-axis angular rate sensing for small spacecrafts. Large tilt angle of its rotor and de-tuned spin rate lead to a complex and non-linear dynamics as well as difficulties in measuring angular rates. In this paper, the problem of angular rate sensing with the GyroWheel is investigated. Firstly, a simplified rate sensing equation is introduced, and the error characteristics of the method are analyzed. According to the analysis results, a rate sensing principle based on torque balance theory is developed, and a practical way to estimate the angular rates within the whole operating range of GyroWheel is provided by using explicit genetic algorithm optimized neural networks. The angular rates can be determined by the measurable values of the GyroWheel (including tilt angles, spin rate and torque coil currents), the weights and the biases of the neural networks. Finally, the simulation results are presented to illustrate the effectiveness of the proposed angular rate sensing method with GyroWheel. View Full-Text
Keywords: GyroWheel; angular rate sensing; large tilt angles; genetic algorithm; artificial neural network GyroWheel; angular rate sensing; large tilt angles; genetic algorithm; artificial neural network
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Zhao, Y.; Zhao, H.; Huo, X.; Yao, Y. Angular Rate Sensing with GyroWheel Using Genetic Algorithm Optimized Neural Networks. Sensors 2017, 17, 1692.

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